Healthcare is one of the most important aspects of human life. Heart disease is known to be one of the deadliest diseases that is hampering many people around the world. Heart disease must be detected early so the loss of lives can be prevented. The availability of large-scale data for medical diagnosis has helped developed complex machine learning and deep learning-based models for automated early diagnosis of heart diseases. The classical approaches have been limited in terms of not generalizing well to new data which have not been seen in the training set. This is indicated by a large gap in training and test accuracies. In this project, we built quantum-classifiers for heart disease prediction.

What it does

Predicts whether a person is having heart disease or not

How we built it

We trained classical models (ANN, CNN, Naive Bayes, Decision Tree, etc) and quantum-classifiers (using Qiskit and Pennylane)

Challenges we ran into

Optimizing circuit, improving accuracy, the right set of parameters, data mapping to quantum

Accomplishments that we're proud of

We were able to achieve an accuracy of more than 70% using quantum-classifiers, and with future improvement in quantum algorithms we can achieve better results

What we learned

How to train quantum-classifier using Qiskit/Pennylane

What's next for QHeart

More optimized circuits and parameters for better results, using image data also to provide more useful outcomes through a UI which can be easily used by medical professionals.

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